Weather forecasting, especially snowfall prediction, was critical in the 2018 Winter Olympics, where the accuracy of the predictions was of key importance for the planning of the different Olympic events. It was a significant challenge for the authors to meet the requirements in time and forecast resolution, while doing their best to be as competitive as possible. All the forecasts were obtained using the Weather Research and Forecasting (WRF) model, executed on the GALGO supercomputer. In order to obtain the best performance and meet the required execution times, different combinations of compilers, Message Passing Interface (MPI) libraries and computing platforms were tested to seek the best combinations. This work proposes an empirical score of special interest to supercomputer maintainers, developers and scientists, which can be useful to obtain the best WRF configuration for their systems. Additionally, we found substantial performance differences when using different combinations of compilers, MPI libraries and hybrid shared memory paradigms, although these differences varied depending on the underlying platform. As conclusion, after all the tests we performed, we chose the combination with Intel compilers, Intel MPI library and OpenMP for the production system tasked to perform the weather forecasts for the Winter Olympic Games.

中文翻译：

---

寻求最佳天气研究和预测模型性能：经验评分方法

天气预报，尤其是降雪量预测，在 2018 年冬季奥运会中至关重要，预测的准确性对于不同奥运会赛事的规划至关重要。对于作者来说，在时间和预测分辨率方面满足要求，同时尽最大努力保持竞争力是一项重大挑战。所有的预测都是使用天气研究和预测 (WRF) 模型获得的，在 GALGO 超级计算机上执行。为了获得最佳性能并满足所需的执行时间，对编译器、消息传递接口 (MPI) 库和计算平台的不同组合进行了测试，以寻求最佳组合。这项工作提出了超级计算机维护人员、开发人员和科学家特别感兴趣的经验分数，这对于为其系统获得最佳 WRF 配置非常有用。此外，我们发现在使用编译器、MPI 库和混合共享内存范式的不同组合时存在显着的性能差异，尽管这些差异因底层平台而异。作为结论，在我们执行了所有测试之后，我们选择了与英特尔编译器、英特尔 MPI 库和 OpenMP 相结合的生产系统，该生产系统的任务是为冬季奥运会执行天气预报。